A hierarchical Bayesian framework was applied for describing variability in pathogen concentration (with associated uncertainty) from presence/absence observations for E. coli O157:H7. Laboratory spiking experiments (method performance) and environmental sample assays were undertaken for a surface drinking water source in France. The concentration estimates were strongly dependent upon the assumed statistical model used (gamma, log-gamma or log-gamma constrained), highlighting the need for a solid theoretical basis for model choice. Bayesian methods facilitate the incorporation of additional data into the statistical analysis; this was illustrated using faecal indicator results of E. coli (Colilert®) to reduce the posterior parameter uncertainty and improve model stability. While conceptually simple, application of these methods is still specialised, hence there is a need for the development of data analysis tools to make Bayesian simulation techniques more accessible for QMRA practitioners.
Quantitative Bayesian predictions of source water concentration for QMRA from presence/absence data for E. coli O157:H7
S. R. Petterson, N. Dumoutier, J. F. Loret, N. J. Ashbolt; Quantitative Bayesian predictions of source water concentration for QMRA from presence/absence data for E. coli O157:H7. Water Sci Technol 1 June 2009; 59 (11): 2245–2252. doi: https://doi.org/10.2166/wst.2009.264
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